Learn Object Detection Tracking and Counting with DL, ML

Name: Learn Object Detection Tracking and Counting with DL, ML
Rating: 3.4 (47 reviews)

Object Detection, Tracking & Counting with OpenCV, Tensorflow, dlib, Machine Learning , Deep Learning

Last updated 2/2021

English

English [Auto],

What you'll learn

9 sections • 19 lectures • 2h 10m total length

Installations - Step-0114:57
Install Anaconda and navigate the setup with step one, create a conda environment (Python 3.6), and install dependencies (dense flow, OpenCV, TensorBoard) in preparation for step two.
Installations - Step-024:46
Install step two guides downloading and organizing denser flow models, setting up the research folder, and launching the object detection notebook to run Python code.
Workflow & System Architecture3:25
Explore the workflow and system architecture for object detection, counting, and tracking using OpenCV and TensorFlow, detailing input data, dense flow, and real-time object counting with pixel locations.

Installation Step-01 - Anaconda Tensorflow Dependencies6:04
Install and configure Anaconda on Windows, create a 64-bit Python environment, launch Jupyter, and install dense flow and dependencies for object detection, tracking, and counting with DL, ML.
Installation Step-02 Dependencies1:22
Please place the protos folder inside your working code directory and change the path as it is.
Installation Step -03 Dependences OpenCV0:59
Please place the protos folder inside your working code directory and change the path as it is.
Installation Step -04 Verifying Installation1:17
Verify the installation by locating the object detection counting folders in the documents folder, confirm white frames are written, and review the generated output video.

Object Detection Tracking Counting Part-0113:40
Here we are using the dlib you can follow the following steps to install dlib

For Mac
$ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
$ brew update
$ echo -e "\n# Homebrew" >> ~/.bash_profile
$ echo "export PATH=/usr/local/bin:$PATH" >> ~/.bash_profile
$ source ~/.bash_profile
$ brew install python python3
$ pip install numpy
$ pip install dlib
Object Counting Part-02 - Person Detection Tracking and Counting - IN OUT16:50
You will learn to write code for Person counting moving inside or outside from a specific position.
You need to install dlib extra and simply can install by $ pip install dlib command to run this code.

Exercise - Visualisation Utility10:37
Exercise - Cumulative Object Counting X-Axis and Y-Axis7:28
Implement cumulative object counting on x-axis and y-axis using a detection graph and frame-by-frame processing, counting objects as they cross a line with detected boxes and scores.
Exercise - Cumulative Object Counting Independently - Webcam - Single Image9:19
Practice cumulative object counting with deep learning on webcam and single images, using color recognition, detection graphs, and target object options to draw boxes and counts.

Exercise - Label Mapping and Model Loading7:16
Learn to validate and build coco-style label maps, create category indexes, and load object detection models by downloading frozen graphs and label maps for accurate tracking and counting.
Exercise - Vehicle Counting with Results3:29
Demonstrate vehicle counting with object detection by crossing a counting line, using denser flow, a detection graph, category index, color recognition, and cumulative counting with ssd mobilenet.
Exercise - Pedestrian Counting with Results3:11
Learn pedestrian counting with TensorFlow object detection, using a backbone and an SSD model to generate a detection graph and count people in a defined area on video.

Introduction of Object Detection, Counting & Tracking
Installation of all prerequisites to write the code for object detection, counting & tracking on Mac Machine
Workflow and System Architecture
Write and Run the Code for
- Write and Run the code for object detection, tracking and counting with dlib
- Write and Run the code for object detection using deep learning with OpenCV
- Write and Run the Code for Cumulative Counting of Objects in Video
- Write and Run the Code for Real-Time Counting of Objects
- Write and Run Code for Object Tracking
- Detect the targeted or all objects
- Count the targeted or all objects
- Predict the colour of targeted or all objects
- Predict the speed of targeted or all objects
- Export results in csv file